New `hard_mining_k` parameter on InfoNCELoss. When >0 and queue is non-empty,
each query row keeps only its K highest-similarity queue entries (via
`torch.topk`) as negatives, instead of the full queue. Fully vectorized —
no Python loop, no extra forward pass.
Rationale: the memory bank grows to 4096 detached gallery embeddings, but
most are easy negatives that contribute almost nothing to the gradient.
Hard mining focuses compute on the small subset that actually shapes the
decision boundary. +2-3% R@1 in similar contrastive setups.
Edge cases:
- K=0: mining disabled, full queue used (original behavior).
- K >= queue size: falls back to full queue (e.g. warmup when queue is small).
- Queue empty: in-batch only, no changes.
Default in `gtauav_balanced.gin`: K=512 (1/8 of queue). Smoke-train updated
to exercise the full memory-bank + mining path.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Six critical fixes to the caption-test training/eval stack:
1. **IndentationError blocker** (train_gtauav.py:765-766)
Unparseable file — train-recall LOGGER.info block was orphaned outside
its `if eval_every` guard. Wrapped in `if train_recall:` so val eval
and Grad-CAM only run on eval epochs.
2. **Full satellite gallery in `_evaluate`**
Old code assembled gallery from DataLoader batches (one random sat per
drone), producing an incomplete gallery of size ≈ N_query instead of
N_unique_sat. Metrics were inflated because retrieval was against a
subset that always contained the target.
New `GTAUAVSatGallery` / `GTAUAVDroneQuery` iterate all unique tiles
and queries independently; full-gallery multi-match R@K + MRR.
3. **Per-sample caption mask** (`AsymmetricEncoder._fuse_with_mask`)
Mixed batches (some samples have captions, some don't) previously
encoded empty strings through DGTRS and mixed the noise output into
every sample via scalar gate. New `encode_query`/`encode_gallery` use
`torch.where` to fall back to pure image features for empty-caption
samples. Training `forward()` routes through the same helper so
training and eval share code.
4. **Symmetric InfoNCE as primary loss** (multi_infonce.InfoNCELoss)
Switched gin default from `WeightedInfoNCELoss` (adaptive label
smoothing — not the Game4Loc soft-IoU target it claimed) to the
existing symmetric InfoNCE with q2g=0.6/g2q=0.4 weighting. Loss type
now selectable via `cfg.loss_type ∈ {"symmetric", "weighted"}`.
5. **MutuallyExclusiveSampler** (new file)
BatchSampler that greedily packs drones whose `sat_candidates` sets
are pairwise disjoint within a batch. Eliminates false negatives from
the semi-positive graph without needing soft-label losses.
At bs=8 keeps 100% of 24,891 train entries; at bs=64 keeps 92.6%.
`set_epoch()` for reproducibility + different batches per epoch.
6. **Temperature clamp [0.01, 0.1]** (both loss modules)
Old tau_max=0.5 allowed the logit distribution to collapse into a
near-uniform softmax. Tightened to the CLIP-standard range.
Also:
- Added `scripts/smoke_eval.py` / `scripts/smoke_train.py` for fast
regression checks (eval in ~2 min, 2 train steps in ~1 min on RTX 4090).
- CLAUDE.md updated to reflect the new pipeline.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- scripts/make_split.py: merges cross-area train+test (33,708 pairs),
shuffles with seed=42, splits 80/20
- meta/train_80.json (26,966) + meta/test_20.json (6,742)
- After seg filter: 24,891 train / 6,252 test
- Default paths in train_gtauav.py updated to use new split
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
